Cost and Actual Causation
This work addresses a foundational issue in philosophy and AI for researchers and practitioners dealing with causal reasoning, but it is incremental as it builds on existing causal modeling frameworks.
The paper tackles the problem of defining actual causation by proposing that its purpose is to minimize costs in intervention practice, and tests the definition on 66 causal cases, showing it fits intuition better than other causal modeling definitions.
I propose the purpose our concept of actual causation serves is minimizing various cost in intervention practice. Actual causation has three features: nonredundant sufficiency, continuity and abnormality; these features correspond to the minimization of exploitative cost, exploratory cost and risk cost in intervention practice. Incorporating these three features, a definition of actual causation is given. I test the definition in 66 causal cases from actual causation literature and show that this definition's application fit intuition better than some other causal modelling based definitions.